The impact of investor sentiment on stock liquidity of listed companies in China

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Researchers have scrutinized the link between investor sentiment and stock market liquidity globally, yet few have delved into this dynamic in emerging markets, especially China. Utilizing a sample of 1,839 publicly listed companies in China from 2010 to 2019, this study applies firm- and year-fixed-effects models to explore the nexus between investor sentiment and stock illiquidity, employing the Amihud measure for stock illiquidity assessment. The outcomes of these fixed-effect regressions illustrate a significantly positive relationship between investor sentiment and stock liquidity in the Chinese market. The positive link is more evident in scenarios characterized by high firm leverage, rapid revenue growth, larger corporations, greater institutional ownership, higher stock volatility, and lower book-to-market ratios. Intriguingly, this analysis incorporates the quadratic term of investor sentiment to examine the potential for a nonlinear dynamic between stock illiquidity and investor sentiment. The findings elucidate that the effect of investor sentiment on stock liquidity diminishes at elevated levels of sentiment, revealing a nonlinear inverse U-shaped relationship. The positive correlation between investor sentiment and stock liquidity persists across the three divisions of the Chinese Shenzhen Stock Exchange and remains robust using alternative liquidity measures, such as Roll’s impact and zeros impact. Addressing causality concerns, current investor sentiment appears to influence subsequent liquidity levels. These results provide valuable perspectives for policymakers, business executives, and investors in the stock market.

Acknowledgment
This research was funded by the Department of Education of Zhejiang Province General Program [Y202353438], the Wenzhou Association for Science and Technology—Service and Technology Innovation Program [jczc0254], the Wenzhou-Kean University Student Partnering with Faculty Research Program [WKUSPF2023004], and the Wenzhou-Kean University International Collaborative Research Program [ICRP2023002].

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    • Table 1. Descriptive statistics
    • Table 2. Pairwise correlations
    • Table 3. Baseline regressions
    • Table 4. Nonlinear quadratic regressions
    • Table 5. Moderating effects of control variables
    • Table 6. Different stock submarkets
    • Table 7. Alternative illiquidity measures
    • Table 8. Changes in illiquidity and sentiment
    • Table 9. Lead illiquidity measure
    • Conceptualization
      Lu Xu
    • Data curation
      Lu Xu
    • Formal Analysis
      Lu Xu, Chunxiao Xue, Jianing Zhang
    • Investigation
      Lu Xu, Chunxiao Xue, Jianing Zhang
    • Methodology
      Lu Xu, Chunxiao Xue, Jianing Zhang
    • Resources
      Lu Xu
    • Software
      Lu Xu
    • Validation
      Lu Xu, Chunxiao Xue, Jianing Zhang
    • Writing – original draft
      Lu Xu
    • Funding acquisition
      Chunxiao Xue, Jianing Zhang
    • Project administration
      Chunxiao Xue, Jianing Zhang
    • Supervision
      Chunxiao Xue, Jianing Zhang
    • Writing – review & editing
      Chunxiao Xue, Jianing Zhang